Amotivation is a common phenotype of major depressive disorder and schizophrenia, which are clinically distinct disorders. Effective treatment targets and strategies can be discovered by examining the dopaminergic reward network function underlying amotivation between these disorders. We conducted an fMRI study in healthy human participants and medicated patients with depression and schizophrenia using an effort-based reinforcement task. We examined regional activations related to reward type (positive and negative reinforcement), effort level, and their composite value, as well as resting-state functional connectivities within the meso–striatal–prefrontal pathway. We found that integrated reward and effort values of low effort-positive reinforcement and high effort-negative reinforcement were behaviorally anticipated and represented in the putamen and medial orbitofrontal cortex activities. Patients with schizophrenia and depression did not show anticipation-related and work-related reaction time reductions, respectively. Greater amotivation severity correlated with smaller work-related putamen activity changes according to reward type in schizophrenia and effort level in depression. Patients with schizophrenia showed feedback-related putamen hyperactivity of low effort compared with healthy controls and depressed patients. The strength of medial orbitofrontal-striatal functional connectivity predicted work-related reaction time reduction of high effort negative reinforcement in healthy controls and amotivation severity in both patients with schizophrenia and those with depression. Patients with depression showed deficient medial orbitofrontal-striatal functional connectivity compared with healthy controls and patients with schizophrenia. These results indicate that amotivation in depression and schizophrenia involves different pathophysiology in the prefrontal-striatal circuitry.SIGNIFICANCE STATEMENTAmotivation is present in both depression and schizophrenia. However, treatment involves the use of drugs that enhance serotonin activity in depression and inhibit serotonin and dopamine activity in schizophrenia. Understanding how motivation processed in the mesocorticolimbic and nigostriatal pathways is affected in depression and schizophrenia is important in discovering treatment targets and strategies for amotivation. To our knowledge, this is the first study to compare patients with depression and schizophrenia in a common functional construct. By using an effort-based reinforcement task and examining resting-state functional connectivity in the dopaminergic network, we propose that difference in striato–orbitofrontal dysfunction in effort-based reinforcement between depression and schizophrenia may be related to differences in the extent of functional dysconnectivity in the dopaminergic pathway.

Background: Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.Methods: We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics’ odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).Results: LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.Conclusion: Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.

Background: Mild cognitive impairment (MCI) may occur due to several forms of neurodegenerative diseases and non-degenerative conditions and is associated with cognitive impairment that does not affect everyday activities. For a timely diagnosis of MCI to prevent progression to dementia, a screening tool of fast, low-cost and easy access is needed. Recent research on eye movement hints it a potential application for the MCI screening. However, the precise extent of cognitive function decline and eye-movement control alterations in patients with MCI is still unclear.Objective: This study examined executive control deficits and saccade behavioral changes in patients with MCI using comprehensive neuropsychological assessment and interleaved saccade paradigms.Methods: Patients with MCI (n = 79) and age-matched cognitively healthy controls (HC) (n = 170) completed four saccadic eye-movement paradigms: prosaccade (PS)/antisaccade (AS), Go/No-go, and a battery of neuropsychological tests.Results: The findings revealed significantly longer latency in patients with MCI than in HC during the PS task. Additionally, patients with MCI had a lower proportion of correct responses and a marked increase in inhibition errors for both PS/AS and Go/No-go tasks. Furthermore, when patients with MCI made errors, they failed to self-correct many of these inhibition errors. In addition to the increase in inhibition errors and uncorrected inhibition errors, patients with MCI demonstrated a trend toward increased correction latencies. We also showed a relationship between neuropsychological scores and correct and error saccade responses.Conclusion: Our results demonstrate that, similar to patients with Alzheimer’s dementia (AD), patients with MCI generate a high proportion of erroneous saccades toward the prepotent target and fail to self-correct many of these errors, which is consistent with an impairment of inhibitory control and error monitoring.Significance: The interleaved PS/AS and Go/No-go paradigms are sensitive and objective at detecting subtle cognitive deficits and saccade changes in MCI, indicating that these saccadic eye movement paradigms have clinical potential as a screening tool for MCI.

Diffusion properties of the thalamocortical pathways were compared between 30 schizophrenic patients and 22 healthy age-matched and sex-matched volunteers. T1-weighted and diffusion tensor images were used for cortical parcellation and probabilistic tractography to generate population-based thalamocortical pathway maps. The weighted fractional anisotropy and the longitudinal and transversal diffusivity of each pathway were calculated for individual participants. Patients with schizophrenia demonstrated decreased anisotropy and increased longitudinal and transversal diffusivity within the pathways from the mediodorsal nucleus and the pulvinar to the orbitofrontal and parietal-occipital-temporal lobes. Anisotropy decreases and diffusivity increases were correlated with positive and negative syndrome scale scores. Alterations in global diffusion properties within the thalamocortical pathways imply the disruption of brain circuitry in schizophrenic patients.